DEVELOPMENT OF AN APPROACH TO INCORPORATE PAVEMENT ... · 2. An augmented matrix was presented that modifies the initial pavement treatments based on whether the structural condition

PE 2019

DEVELOPMENT OF AN APPROACH TO INCORPORATE

PAVEMENT STRUCTURAL CONDITION INTO THE

TREATMENT SELECTION PROCESS AT THE

NETWORK-LEVEL

By

Shivesh Shrestha

Virginia Tech

PE 2019 ResultsObjective

Contents

1. Introduction

2. Objective

3. Methodology

4. Results

5. Conclusion

ConclusionMethodologyIntroduction

PE 2019

Introduction

ResultsObjective ConclusionMethodologyIntroduction

Rada et al. 2011 2011

2000s World’s first Doppler-based Traffic Speed Deflectometer (TSD),

configured with 4 Doppler sensors, was developed and patented by

Greenwood Engineering.

2012 Flintsch et al. 2012

TSD was a promising device

for network level pavement

management applications. }commissioned by FHWA

Second Strategic Highway Research Program 2 (SHRP 2) R06(F)

PE 2019

Introduction


2013Katicha et al. 2017 – Federal Highway Administration (FHWA)

initiated a pooled fund project to perform field demonstration of

the TSD. (9 participating states)

The study concluded that the TSD was capable of differentiating

between relatively structurally strong and weak pavement sections.

2016 Rada et al. 2016 – FHWA Report

Relationship between the corresponding deflection basin indices and pavement

structural response. Recommended deflection indices: SCI300 and DSI.

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Introduction


Pavement Surface

Distresses

Preliminary

Treatment

Selection

∙∙∙∙∙∙

Fatigue Cracking

Transverse Cracking

Rutting

Patching

Decision Trees

Final

Treatment

Selection

Traffic Level

Structural

Integrity

Construction

History + +

Decision

Matrices

+

Condition

Index

Preliminary

Treatment

Selection

Decision Trees

Final Treatment Selection

Traffic Level

Structural

Integrity

Construction

History + +

Decision

Matrices

+

Condition

Index

Preliminary Treatment Selection

Enhancement

• Katicha et al. 2017

• Rada et al. 2016

VDOT’s enhanced decision making process

PE 2019

Introduction


2017

Virginia Department of Transportation – Traffic Speed Deflectometer

Network Level Structural Evaluation with the TSD

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Objective

Develop a treatment selection matrix that takes into account the pavement structural condition and augments the existing

treatment selection process based solely on the observed surface condition.


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Methodology

1. Data Collection and Processing

2. Development of Deterioration Models

3. Incorporating Structural Condition Information into the PMS Decision making Process


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Interstate Roads: 1500 miles Primary Roads: 2530 miles

Virginia

I81, I95 and I64 US460, US360, US220, US60, US58, US29, US17, SR28, SR288

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Deflection Slope:

h

v

V

VS

Where,S = the deflection slopeVv = the vertical pavement deflection velocityVh =the vehicle horizontal velocity

x

dyysxd

Where,s(y) = slope at location y measured from the applied loadd(x) = deflection at location x measured from the applied load

Deflection:

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• Deflections measurements: D0, D100, D200, D300, D600, D900, D1500

• Structural Condition parameters:

1. SCI300 = d0 – d300 : Surface Curvature Index

2. d0 : Deflection at 0mm

3. SNeff : Effective structural number

(by Nasimifar et al. 2019)

• Temperature Adjustment to 20°C


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• Temperature Adjustment for SCI300 (by Nasimifar et al. 2018)


Ref Ref0.05014 0.019049 log log

Ref

0.05014 0.019049 log log

10

10

AC

AC

T T h

T T h

T

SCI

SCI

Where, =Temperature Adjustment Factor

SCIRef= Adjusted SCI300 at reference temperature

TRef = Reference temperature in °C

hAC= Asphalt layer thickness, mm

T= Mid-depth AC layer temperature at time of measurement in °C

= Latitude of location of measurement (within 30 to 50 degrees)

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• Temperature Adjustment for D0 (AASHTO temperature adjustment charts)


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• Temperature Adjustment for SNeff (by Nasimifar et al. 2019)

• Temperature Adjusted d0 was used to calculate the SIP

• No temperature adjustment was performed for d1.5Hp


32

1

kk

eff pSN k SIP H

K1, K2 and K3 are 0.4369, -0.4768, and 0.8182

0 1.5 pHSIP d d

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VDOT pavement condition index: Critical condition Index (CCI)


Load Related Distress (LDR):

Critical Condition Index (CCI)(The lowest of the LDR or NDR )

Non Load Related Distress (NDR):

Transverse cracking

Longitudinal cracking

Bleeding

Alligator cracking

Patching

Potholes

Delamination

Rutting

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2. Development of Deterioration ModelsDeterioration = 100- CCI, 100-LDR, 100-NDR


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2. Development of Deterioration Models

Deterioration equations where developed for the LDR, NDR, and CCI with SCI300, D0 and SNeff.


0 1 2

0 1 3

100 exp log log 300

100 exp 1 300 log

LDR Age Age SCI

SCI Age

Where, LDR = load related distress

Age = pavement age calculated as the difference between the year at which the LDR is observed minus the

year of the last applied treatment recorded in the PMS

SCI300 = Surface Curvature Index

0, 1, 2, and 3 = regression coefficients with 3 = 2/ 1.

PE 2019

3. Data Incorporating Structural Condition Information into the PMS Decision Process


Pavement Surface

Distresses

Preliminary

Treatment

Selection

∙∙∙∙∙∙

Fatigue Cracking

Transverse Cracking

Rutting

Patching

Decision Trees

Final

Treatment

Selection

Traffic Level

Structural

Integrity

Construction

History + +

Decision

Matrices

+

Condition

Index

Preliminary

Treatment

Selection

Decision Trees

Final Treatment Selection

Traffic Level

Structural

Integrity

Construction

History + +

Decision

Matrices

+

Condition

Index

Preliminary Treatment Selection

Enhancement

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Results


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Results


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Results


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Results


• In general, the results show that sections with higher SCI300 (weaker sections) have a higher rate of deterioration than those with a lower SCI300 (stronger sections). (with a few exceptions)

• Three structural condition parameters, SCI300, d0, and SNeffwere investigate and it was found that SCI300 is the best parameter to use in a deterioration model.

• Structural condition has a significant impact on pavement deterioration.

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Results


The threshold for Fair and Strong is the 25th percentile of SCI300 and

the threshold between Fair and Weak is the 75th percentile of SCI300.

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Results


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Conclusion

1. In general, the deterioration models behaved as expected with the

deterioration rate increasing for structurally weaker sections. (with the exception of a small number of the analyzed roads)

It would be beneficial to incorporate the structural condition into the treatment selection decision process.


PE 2019

Conclusion


2. An augmented matrix was presented that modifies the initial pavement treatments based on whether the structural condition is Strong, Fair, or Weak.

The thresholds for determining the state of the pavement structural condition were based on the 25th and 75th percentile of SCI300 values of each route.

PE 2019

Conclusion


3. A treatment selection matrix that incorporates the pavement structural condition into treatment selection tool was presented.

• For the structurally Strong sections CM, RM, and RC were modified to a lighter treatment.

• For the Fair category, the treatments are kept the same as recommended based on the surface condition.

• For the structurally Weak sections CM and RM were modified to a heavier treatment category while PM was modified to DN.

PE 2019

References1. Rada, G., et al. Moving pavement deflection testing devices: state of the technology

and best uses. in Proc., 8th International Conference on Managing Pavement Assets, Santiago, Chile. 2011. Citeseer.

2. Flintsch, G., Katicha, S., Bryce, J., Ferne, B., Nell, S., & Diefenderfer, B. (2013). Assessment of continuous pavement deflection measuring technologies (No. SHRP 2 Report S2-R06F-RW-1).

3. Katicha, S., et al., Demonstration of Network Level Structural Evaluation With Traffic Speed Deflectometer: Final Report. Prepared for the Federal Highway Administration, 2017.

4. Rada, G. R., Nazarian, S., Visintine, B. A., Siddharthan, R. V., & Thyagarajan, S. (2016). Pavement structural evaluation at the network level (No. FHWA-HRT-15-074). United States. Federal Highway Administration. Office of Infrastructure Research and Development.

5. Nasimifar, M., et al., Temperature adjustment of Surface Curvature Index from Traffic Speed Deflectometer measurements. International Journal of Pavement Engineering, 2018: p. 1-11.

6. Nasimifar, M., Thyagarajan, S., Chaudhari, S., & Sivaneswaran, N. (2019). Pavement Structural Capacity from Traffic Speed Deflectometer for Network Level Pavement Management System Application. Transportation Research Record, 2673(2), 456-465.

PE 2019Thank you

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DEVELOPMENT OF AN APPROACH TO INCORPORATE PAVEMENT ... · 2. An augmented matrix was presented that modifies the initial pavement treatments based on whether the structural condition